A method of a related general type is known from, e.g., DE 10 2007 013 023 A1, in which the surroundings of the vehicle are covered or monitored by a surroundings sensor and are subdivided, for the detection of objects in the surroundings of the vehicle, into grid cells. To each of these grid cells, a value indicating the probability of occupancy for the presence of an object in the respective grid cell is assigned, wherein the value of 0 or a low value being in the range of probability near 0 is assigned to a grid cell that has no detected object or that is hidden, whereas a high value (e.g., between 0.5 and 1) is assigned to an occupied grid cell. In particular, in this method known from DE 10 2007 013 023 A1, a value depending on the distance between a free grid cell and the vehicle is assigned to each grid cell, i.e., the greater the distance to the free grid cell, the higher the selected probability of occupancy.
The coordinate system of the grid-based surroundings map generated by this known method according to DE 10 2007 013 023 A1 is connected to the global coordinate system in a rotationally fixed manner so that the vehicle representation is moved on the two-dimensional grid structure of the surroundings map when the actual vehicle moves.
This grid-based surroundings map generated in this manner according to DE 10 2007 013 023 A1 is used to detect a pavement, a vehicle corridor and/or pavement boundaries. For this purpose, a region on the grid-based surroundings map in which the probabilities of occupancy are below a predetermined value (e.g., 0.1) is determined in a first step. Within this region, a center line extending in the longitudinal direction of the vehicle is determined and subdivided into several partial lines. These partial lines are then displaced, perpendicularly to the direction of the center line, to both sides of the vehicle until they are displaced to grid cells whose probabilities of occupancy exceed a particular value, e.g., 0.5. These partial lines displaced in this manner are connected to each other, which is followed by a check whether the connecting line resulting therefrom describes a model given for the presence of a pavement, a vehicle corridor and/or a pavement boundary, e.g., a straight line, a clothoid, a polygon, a polynomial, a parabola, or a spline.
Finally, it is also possible, by means of the grid-based surroundings map generated according to DE 10 2007 013 023 A1, to locate the ego-vehicle on this surroundings map by means of the surroundings sensor.
However, the results of the method for determining a course of a traffic lane for a vehicle described in DE 10 2007 013 023 A1 are not satisfactory in all traffic situations. In particular, the results are not satisfactory when there are no or too few measurements for updating the grid-based surroundings map due to low driving speed or due to a covered (in particular, by vehicles driving ahead of the ego-vehicle) visual range of the surroundings sensor.
Another method for detecting and tracking structures that demarcate a traffic lane and/or a pavement is known from, e.g., DE 10 2009 003 697 A1, in which the surroundings of a vehicle are covered by means of a camera and an image-processing algorithm is used that analyzes, in the acquired images, structures that are characteristic of a traffic lane and/or a pavement and the course thereof, e.g., pavement markings or pavement verges, such as crash barriers and the like. The image-processing algorithms employed detect markings especially due to the dark-to-bright/bright-to-dark transitions between the pavement surface and the pavement markings. Furthermore, the images are searched for structures that exhibit the highest contrast since such structures are mostly generated by the above-mentioned transitions.
In these known methods that detect bright-to-dark/dark-to-bright transitions and supply them to a filtering algorithm, filtering algorithms are used that depend on the speed of the vehicle, such as a Kalman filter using a clothoid model. With such a model-based filter, the estimation of the lane position is based on two data inputs: from the position of the measurements and from the vehicle's own motion. If no more measurements are received when the speed of the vehicle is low or the visual range is covered by the vehicle driving ahead of the ego-vehicle, tracking may continue and will only use the vehicle's own motion in this case.
One problem of this procedure consists in the fact that at a low vehicle speed, an incorrectly assumed angle or an incorrectly estimated curvature of the course of the lane results in a “turning-away” of the lane, which means that, e.g., a bend is estimated instead of a straight line or a straight line is estimated instead of a bend. Therefore, also such a method can only be employed at higher vehicle speeds.